Browsing by Author "Akyol, Ali Alp"

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Open Access
Anomaly detection in diverse sensor networks using machine learning
(2022-01) Akyol, Ali Alp
Earthquake precursor detection is one of the oldest research areas that has the potential of saving human lives. Recent studies have enlightened the fact that strong seismic activities and earthquakes affect the electron distribution of the ionosphere. These effects are clearly observable on the ionospheric Total Electron Content (TEC) that shall be measured by using the satellite position data of the Global Navigation Satellite System (GNSS). In this dissertation, several earthquake precursor detection techniques are proposed and their precursor detection performances are investigated on TEC data obtained from different sensor networks. First, a model based earthquake precursor detection technique is proposed to detect precursors of the earthquakes with magnitudes greater than 5 in the vicinity of Turkey. Precursor detection and TEC reliability signals are generated by using ionospheric TEC variations. These signals are thresholded to obtain earthquake precursor decisions. Earthquake precursor detections are made by using Particle Swarm Optimization (PSO) technique on these precursor decisions. Performance evaluations show that the proposed technique is able to detect 14 out of 23 earthquake precursors of magnitude larger than 5 in Richter scale while generating 8 false precursor decisions. Second, a machine learning based earthquake precursor detection technique, EQ-PD is proposed to detect precursors of the earthquakes with magnitudes greater than 4 in the vicinity of Italy. Spatial and spatio-temporal anomaly detection thresholds are obtained by using the statistics of TEC variation during seismically active times and applied on TEC variation based anomaly detection signal to form precursor decisions. Resulting spatial and spatio-temporal anomaly decisions are fed to a Support Vector Machine (SVM) classifier to generate earthquake precursor detections. When the precursor detection performance of the EQ-PD is investigated, it is observed that the technique is able to detect 22 out of 24 earthquake precursors while generating 13 false precursor decisions during 147 days of no-seismic activity. Last, a deep learning based earthquake precursor detection technique, DLPD is proposed to detect precursors of the earthquakes with magnitudes greater than 5.4 in the vicinity Anatolia region. The DL-PD technique utilizes a deep neural network with spatio-temporal Global Ionospheric Map (GIM)-TEC data estimation capabilities. GIM-TEC anomaly score is obtained by comparing GIMTEC estimates with GIM-TEC recordings. Earthquake precursor detections are generated by thresholding the GIM-TEC anomaly scores. Precursor detection performance evaluations show that DL-PD shall detect 5 out of 7 earthquake precursors while generating 1 false precursor decision during 416 days of noseismic activity.
Open Access
Detection of jammers in range-doppler images generated in DTED based radar simulator using convolutional neural networks
(IEEE - Institute of Electrical and Electronics Engineers, 2023-08-28) Şahinbay, H. E.; Akyol, Ali Alp; Özdemir, Ö.
Airborne radars have a variety of air-to-air and air-to-ground missions. In both air-to-air and air-to-ground target detection missions, ground clutter reflections are received from the main beam and side lobes of the radar. The effects of this clutter can be clearly seen in the radar range-Doppler maps. In addition, there may be other sources in the environment that distort the radar's range-Doppler maps. These sources can be categorized as jammer and interference signals. They distord the range-Doppler maps, making target detection more difficult, interfering with target detection and, in some cases, leading to false target detection. The detection of jammer and interference signals, which are the source of this situation, is of critical importance for the operators controlling the platform. It is often not possible for operators to quickly detect and classify these jamming signals. Deep learning methods, which have recently been used in every field, can achieve much faster and robust target detection and classification results compared to humans. In this study, the success of a Convolutional Neural Network based technique, which is one of the deep learning methods, in detecting and classifying jammer and interference signals is investigated.
Open Access
Investigation on the reliability of earthquake prediction based on ionospheric electron content variation
(ISIF, 2013-07) Akyol, Ali Alp; Arıkan, Orhan; Arıkan F.; Deviren, M. N.
Due to lack of statistical reliability analysis of earthquake precursors, earthquake prediction from ionospheric parameters is considered to be controversial. In this study, reliability of earthquake prediction is investigated using dense TEC data estimated from the Turkish National Permanent GPS Network (TNPGN- Active). © 2013 ISIF ( Intl Society of Information Fusi.
Open Access
Investigation on the reliability of earthquake prediction based on ionospheric electron content variation
(2013) Akyol, Ali Alp
Ionosphere region of Earth’s upper atmosphere ranging from 90 km to 1000 km altitude, has a significant effect on military and civilian communications, satellite communications and positioning systems. Solar, geomagnetic, gravitational and seismic activities cause variations in the electron distribution of the atmosphere. The number of electrons within a vertical column of 1 m2 cross section, which is called as Total Electron Content (TEC), is a measurable feature of the ionosphere that provides valuable information about the ionosphere. TEC can be measured fast and accurately by using the phase difference between transmitted satellite positioning signals such as in the Global Positioning System (GPS). To investigate the reliability of earthquake prediction based on detection of local ionospheric anomalies, TEC measurements obtained from a network of GPS receivers over a period of 2 years in 2010 and 2011 are used to generate detection signals. For a day of interest, after selecting a receiver station surrounding GPS stations that are located within 150 km of the chosen station used to estimate TEC measurements at the chosen station. In one of the proposed techniques, detection of ionospheric anomalies is based on distance between measured TEC and its estimate. Detection threshold is obtained based on statistical variation of this distance for the days with insignificant seismic activities. Also, another detection technique based on temporal variation of TEC measurements is proposed. Both individual and fused detection performances of these techniques are investigated for a given level of false alarms. It is observed that the fused detection has superior performance and able to detect 15 out of 23 earthquakes of magnitude larger than 5 in Richter scale while generating 8 false alarms.
Open Access
İyonküre elektron içeriği kullanılarak deprem öncül tespit sinyali oluşturulması
(IEEE, 2014-04) Akyol, Ali Alp; Arıkan, Orhan; Arıkan, F.
Sismik olayların iyonküredeki elektron dağılımını etkilediği ve bu etkinin bir kaynağının sismik hareketlilik öncesi kayaçların sıkışması sonucu kayaç yüzeylerinde oluşan elektrik alanın yol açtığı iyonlaşma olduğu yakın zamanda yapılan deneyler ile gösterilmiştir. Sismik olayların yol açtığı bu tür bir etkinin iyonkürenin dinamik yapısı içinde güvenilir şekilde tespit edilebilmesi depremlerin erken tahminini sağlayabilecek önemdedir. Bu çalışmada bir YKS alıcı ağından düzenli olarak elde edilen Toplam Elektron İçeriği (TEİ) ölçümleri kullanılarak iyonküredeki yerel değişimlerin tespit edilmesine yönelik bir teknik geliştirilmiş ve bu tekniğin güvenilir bir deprem öncül sinyali üretip üretemediği 2010 ve 2011 yıllarını kapsayan bir zaman aralığında test edilmiştir. Geliştirilen deprem öncül tespiti tekniğinin bu tarih aralığında Türkiye’de Richter ölçeğinde 5 ve üzeri büyüklükte meydana gelen 23 depremin 15’ini tespit edebildiği ve 8 yanlış alarm verdiği gözlemlenmiştir.
Open Access
A machine learning‐based detection of earthquake precursors using ionospheric data
(Blackwell Publishing, 2020) Akyol, Ali Alp; Arıkan, Orhan; Arıkan, F.
Detection of precursors of strong earthquakes is a challenging research area. Recently, it has been shown that strong earthquakes affect electron distribution in the regional ionosphere with indirectly observable changes in the ionospheric delays of GPS signals. Especially, the total electron content (TEC) estimated from GPS data can be used in the seismic precursor detection for strong earthquakes. Although physical mechanisms are not well understood yet, GPS-based seismic precursors can be observed days prior to the occurrence of the earthquake. In this study, a novel machine learning-based technique, EQ-PD, is proposed for detection of earthquake precursors in near real time based on GPS-TEC data along with daily geomagnetic indices. The proposed EQ-PD technique utilizes support vector machine (SVM) classifier to decide whether an observed spatiotemporal anomaly is related to an earthquake precursor or not. The data fed to the classifier are composed of spatiotemporal variability map of a region. Performance of the EQ-PD technique is demonstrated in a case study over a region covering Italy in between the dates of 1 January 2014 and 30 September 2016. The data are partitioned into three nonoverlapping time periods, that are used for training, validation, and test of detecting precursors of earthquakes with magnitudes above 4 in Richter scale. The EQ-PD technique is able to detect precursors in 17 out of 21 earthquakes while generating 7 false alarms during the validation period of 266 days and 22 out of 24 earthquakes while generating 13 false alarms during the test period of 282 days.